





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


_A					:				Tuple				=			logging.get_logger(__name__)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			b.T
    SCREAMING_SNAKE_CASE__											=			np.sum(np.square(lowerCAmelCase_	) ,							axis=1	)
    SCREAMING_SNAKE_CASE__											=			np.sum(np.square(lowerCAmelCase_	) ,							axis=0	)
    SCREAMING_SNAKE_CASE__											=			np.matmul(lowerCAmelCase_ ,							lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			aa[:, None] - 2 * ab + ba[None, :]
    return d


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			x.reshape(-1 ,							3	)
    SCREAMING_SNAKE_CASE__											=			squared_euclidean_distance(lowerCAmelCase_ ,							lowerCAmelCase_	)
    return np.argmin(lowerCAmelCase_ ,							axis=1	)



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	["""pixel_values"""]



    def __init__(       self   ,				A_ = None   ,				A_ = True   ,				A_ = None   ,				A_ = PILImageResampling.BILINEAR   ,				A_ = True   ,				A_ = True   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(**A_     )
        SCREAMING_SNAKE_CASE__											=			size if size is not None else {'''height''': 2_56, '''width''': 2_56}
        SCREAMING_SNAKE_CASE__											=			get_size_dict(A_     )
        SCREAMING_SNAKE_CASE__											=			np.array(A_     ) if clusters is not None else None
        SCREAMING_SNAKE_CASE__											=			do_resize
        SCREAMING_SNAKE_CASE__											=			size
        SCREAMING_SNAKE_CASE__											=			resample
        SCREAMING_SNAKE_CASE__											=			do_normalize
        SCREAMING_SNAKE_CASE__											=			do_color_quantize



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = PILImageResampling.BILINEAR   ,				A_ = None   ,				**A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			get_size_dict(A_     )
        if "height" not in size or "width" not in size:
            raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''     )
        return resize(
            A_   ,				size=(size['''height'''], size['''width'''])   ,				resample=A_   ,				data_format=A_   ,				**A_     )



    def  lowercase_					(       self   ,				A_   ,				A_ = None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			rescale(image=A_   ,				scale=1 / 127.5   ,				data_format=A_     )
        SCREAMING_SNAKE_CASE__											=			image - 1
        return image




    def  lowercase_					(       self   ,				A_   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = ChannelDimension.FIRST   ,				**A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE__											=			size if size is not None else self.size
        SCREAMING_SNAKE_CASE__											=			get_size_dict(A_     )
        SCREAMING_SNAKE_CASE__											=			resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE__											=			do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE__											=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        SCREAMING_SNAKE_CASE__											=			clusters if clusters is not None else self.clusters
        SCREAMING_SNAKE_CASE__											=			np.array(A_     )

        SCREAMING_SNAKE_CASE__											=			make_list_of_images(A_     )

        if not valid_images(A_     ):
            raise ValueError(
                '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                '''torch.Tensor, tf.Tensor or jax.ndarray.'''     )

        if do_resize and size is None or resample is None:
            raise ValueError('''Size and resample must be specified if do_resize is True.'''     )

        if do_color_quantize and clusters is None:
            raise ValueError('''Clusters must be specified if do_color_quantize is True.'''     )

        # All transformations expect numpy arrays.
        SCREAMING_SNAKE_CASE__											=			[to_numpy_array(A_     ) for image in images]

        if do_resize:
            SCREAMING_SNAKE_CASE__											=			[self.resize(image=A_   ,				size=A_   ,				resample=A_     ) for image in images]

        if do_normalize:
            SCREAMING_SNAKE_CASE__											=			[self.normalize(image=A_     ) for image in images]

        if do_color_quantize:
            SCREAMING_SNAKE_CASE__											=			[to_channel_dimension_format(A_   ,				ChannelDimension.LAST     ) for image in images]
            # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
            SCREAMING_SNAKE_CASE__											=			np.array(A_     )
            SCREAMING_SNAKE_CASE__											=			color_quantize(A_   ,				A_     ).reshape(images.shape[:-1]     )

            # flatten to (batch_size, height*width)
            SCREAMING_SNAKE_CASE__											=			images.shape[0]
            SCREAMING_SNAKE_CASE__											=			images.reshape(A_   ,				-1     )

            # We need to convert back to a list of images to keep consistent behaviour across processors.
            SCREAMING_SNAKE_CASE__											=			list(A_     )
        else:
            SCREAMING_SNAKE_CASE__											=			[to_channel_dimension_format(A_   ,				A_     ) for image in images]

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': images}
        return BatchFeature(data=A_   ,				tensor_type=A_     )


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






